This invention relates to data warehouses. Data warehouse systems are very common among organizations for the purpose of monitoring business activities. Some companies have also built additional business applications, such as OLAP (On-Line Analytical Processing) applications, on top of their enterprise data warehouse systems in order to help derive intricate business metrics or to answer ad-hoc business questions.
Though new data is continuously added to an enterprise data warehouse system, these additional business applications are often built on data entities which are independent of the running state of an enterprise data warehouse system. For example, multi-dimensional OLAP cubes are typically built on physical “snapshots” of data warehouse data taken at a particular time and stored on disk.
As the data size of an enterprise data warehouse system gets increasingly larger, organizations have come to realize that they are spending more and more IT (Information Technology) resources on constructing and maintaining these physical snapshots of the enterprise data warehouse system, taken at varying points in time by individual departments with varying sub-structures of the data warehouse. As a result, some organizations are finding that they have been building and maintaining hundreds of physical snapshots of their data warehouse data to support their multi-dimensional OLAP cubes. This requires a large amount of storage resources, as well as personnel costs, and may often be of limited value as large portions of the data become stale over time.